In addition, it has been shown across many other domains such as computer vision that the model performance depends critically on the quantity of training data. Hence, it is anticipated that an increased amount of training data will directly lead to improvements in performance, especially as the U-Net is trained with more examples. Indeed, the results in Table REF  show that a doubling of the amount of provided data lead to an approximate decrease in MAE of about 0.01 m/s. The results also clearly show that the current dataset is still not comprehensive enough for an asymptotic trend to have developed. Interestingly, we note that there appears to be a log-linear trend in the relationship between improvements in model performance and the amount of data provided for this particular range of dataset size. This trend was previously reported in work by [1]}, using a similar U-Net type architecture, suggesting that certain similar dataset and model characteristics could be at play.
<TABLE>